1. Field of the Invention
The present invention relates to a method for allocating resources by utilizing experimental knowledge of a system operator to enhance system performance, and more particularly to a method for allocating resources to user jobs (unit processes) in an electronic computer system.
2. Description of the Prior Art
In resource management, in a computer system, resources in the system are allocated so as to enhance system performance and system response.
In the prior art resource management, in order to effectively utilize the resources in a system, the system operator experimentally determines a resource allocating plan and then, after executing the resource allocating plan, measures the resultant utilization factors of the resources, queue status of the unit processes by jobs and transactions in the system, and resource allocating status. If the measured values deviate from the target values thereof, the system operator modifies the resource allocating plan so as to cause the measurements to approch the target values. However, recent rapid increase in the number of resources to be handled in the resource management (large capacities of main memory, disk or mass storage system (MSS) and multipurpose processing units) involves various problems such that the overhead of the resource management increases (large scaled operating system) and the required memory capacity also increases. In a case where a target value is represented by a given function which is variable dependent on various parameters, for example, a processing time (target value) is represented by a function of the number of queued jobs and the characteristic curve representing the function is variable dependent on various parameters, it is required to optimize those parameters. This is done by the system operator of the computer system by making adjustments in accordance with the operator's experience of operation. Since the unit processes in the system vary in quantity and quality every time, the operator must always monitor the status changes to adjust the parameters as required. Accordingly, automatization to reduce the workload of the operator is highly desired. However, since the adjustment work largely depends on the experience and knowledge of the operator, such automatization has been hard to attain. Further, since the parameters are adjusted in a trial and error fashion each time the system is expanded, it is difficult to automate the work done by the operator.